Hey friends,

Hope you're having a great week so far. This week has been a hectic week for me as we are building a new feature at Staq, pushing for commercial traction, and preparing for something exciting (shared below 😎)!

By the way, we made a big mistake in our startup journey recently and I thought of sharing this with you. Hope you'd find it useful.

Let's get started! 🚀

What's in the hub today?

• Tip: Generalist first, Specialist later
• Mistake: I thought I knew it all
• Learning: Never assume & be complacent
• Book: AI Superpowers
• Quote: Coexistence between humans & AI

1 Tip:

⭐️ Generalist first, Specialist later

A generalist is someone that has knowledge in many areas whereas a specialist knows a lot in one area. Simple as that.

If you're in data science, become a generalist first, before you become a specialist in a certain area (i.e. NLP). Let me explain.

Why be a Generalist first?

Because being a generalist helps you understand the full picture of the whole data science project lifecycle. You get to learn how to:

• Define a problem statement
• Identify data sources and define success metrics
• Build a data pipeline to collect data
• Clean, analyse, visualise data
• Present insights to stakeholders

All these elements are important to make a data science project successful.

Why be a Specialist later?

Because being a specialist makes you irreplaceable and valuable to others for 2 reasons:

• You have expertise in one particular niche that many people don't have.
• Because you come from a generalist experience, you have a broad knowledge and understanding of other parts of the data science workflow.

You’re no longer someone who knows only one thing. Instead, you’re someone who knows many things with a focus on a particular thing that makes you special.

In short, be a generalist first, specialist later as a data scientist.

Are you a generalist or a specialist? Would love to know 🙂

1 Mistake:

Throughout my journey of building Staq, I've made tons of mistakes. One of the biggest mistakes was that I thought I knew it all.

When we first talked to our first customer, we asked many questions and did a lot of customer discovery to understand the problems and the end-to-end process in detail.

The good news? We got tons of learnings and insights from the conversation.

The bad news? We became complacent. We thought we knew it all.

We assumed other customers also face the same problems and have the same process, so we went to other customers and pitched our solution.

No customer discovery. No understanding of their problems and process.

The result? We got lukewarm responses from other customers as they felt our solution was not what they were looking for.

We only realised this mistake one month later. Bad move. 🤦🏻‍♂️

1 Learning:

We learned that:

• We should never assume anything.
• We should never be complacent.
• We should always understand what problems customers are facing and how the end-to-end process works thoroughly before we build or pitch our solution.

Once we realised the mistake we made, we talked to our previous customers again with customer discovery as the main purpose.

We got an important insight from just a few conversations. We went back to our drawing board and designed a solution to solve the problem that most customers are facing.

This time, we got more positive responses from previous customers. Most importantly, we already started doing pilot tests with some of them! 🎉

💰We are fundraising!

After working in stealth mode for so long, I'm excited to share with you that we are preparing to fundraise (seed) around the end of September.

We're currently looking for strategic investors or angel operators to join us. If you or your friends are interested, just reply to this email or hit me up on LinkedIn and we can chat!

1 Book:

​AI Superpowers: China, Silicon Valley, and the New World Order​

This book is written by Dr. Kai-Fu Lee - one of the most prominent figures in AI and he is currently the chairman and CEO of Sinovation Ventures. In his previous life, he was the founding director of Microsoft Research Asia and the president of Google China.

If you're in the data science/AI space, this book is a must-read. It shows you the future of AI and how we can reverse engineer to create the AI future we want.

💡Here are my few takeaways after reading the book:

• China is one of the biggest AI superpowers in the world. How can other countries do the same? These are the 4 elements required:
  • Abundant data
  • Tenacious entrepreneurs to democratise the use of AI
  • Well-trained AI scientists to build complex AI models
  • Supportive policy environment for AI startups
• With the rise of AI, there would be an AI crisis in 2 areas:
  • Job loss - Manual and repeatable jobs would be automated with robots, causing job loss for factory workers, customer service reps, restaurant cooks and many more.
  • Economic Inequality - Developed countries with resources can leverage AI to improve the economy. On the other hand, underdeveloped countries don't have the resources or expertise to leverage AI to boost the productivity and economy. The result?
    • This would create an increasing economic gap between developed and underdeveloped countries.
    • The rich will become richer, and the poor will become poorer. Sad, but true.

Have you read this book? What's your thought on the future of AI in the next 5-10 years?

1 Quote:

This is the quote (also the future) painted by Dr. Kai-Fu Lee.

When we think about AI in the future, most of us tend to be fearful.

• We fear that AI will replace our jobs.
• We fear that AI will go out of our control.
• We fear that AI will cause doomsday to mankind.

🌈 Here's a better future with AI:

• Instead of choosing either humans or AI, we can coexist with AI by having AI complement our mundane and complex jobs. Ultimately, humans still have to make the final decisions. Coexistence, not replacement.

That's all for today

Thanks for reading. I hope you enjoyed today's issue. More than that, I hope it has helped you in some ways and brought you some peace of mind.

You can always write to me by simply replying to this newsletter and we can chat.

See you again next week.

- Admond

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